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Record W2998157188 · doi:10.5045/br.2019.54.4.282

EBV reactivation mimicking a lymphoproliferative disorder associated with ruxolitinib therapy for myelofibrosis

2019· article· en· W2998157188 on OpenAlexaff
Shruti Prem, David Loach, Jeffrey H. Lipton, Rajat Kumar, Vikas Gupta

Bibliographic record

VenueBlood Research · 2019
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineRuxolitinibMyelofibrosisLymphoproliferative disordersImmunologyVirologyDermatologyBone marrowLymphoma

Abstract

fetched live from OpenAlex

L, neutrophils 4.8×10 9 /L, lymphocytes/monocytes 65×10 9 /L, blast count 2×10 9 /L, basophils 1.4×10 9 /L, meta-myelocytes 0.7×10 9 /L, myelocytes 0.7× 10 9 /L, and pro-myelocytes 0.7×10 9 /L.Peripheral blood lymphocyte count had increased rapidly to 65×10 9 /L (previous lymphocyte count ranged from 1.5 to 15×10 9 /L), and he was found to have EBV reactivation with 824,000 bloodresearch.or.kr

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.362
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2019
Admission routes1
Has abstractyes

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